Non-contact system and method for detecting defects in an additive manufacturing process

ABSTRACT

A Pulsed Thermography (PT) system and method is provided utilizing a long duration pulse in combination with a radiant heat shield as a non-destructive testing method for quantitatively measuring defect depths within a 3D printed part and for characterizing layer-by-layer surface defects in the 3D printed part.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority toInternational Patent Application No. PCT/US2018/045406, entitled“NON-CONTACT SYSTEM AND METHOD FOR DETECTING DEFECTS IN AN ADDITIVEMANUFACTURING PROCESS,” filed Aug. 6, 2018 which claims priority to U.S.Provisional Patent Application Ser. No. 62/541,472, entitled“NON-CONTACT SYSTEM AND METHOD FOR DETECTING DEFECTS IN AN ADDITIVEMANUFACTURING PROCESS,” filed Aug. 4, 2017, U.S. Provisional PatentApplication Ser. No. 62/650,727, entitled “SYSTEM AND METHOD FOR THERMALDETECTION OF SUBSURFACE DEFECTS UTILIZING LONG THERMAL INPUT PULSES,”filed Mar. 30, 2018, and U.S. Provisional Patent Application Ser. No.62/654,020, entitled “SYSTEM AND METHOD FOR THERMAL DETECTION OFSUBSURFACE DEFECTS UTILIZING LONG THERMAL INPUT PULSES,” filed Apr. 6,2018, the contents of each of which are hereby incorporated by referenceinto this disclosure.

BACKGROUND OF THE INVENTION

In recent years, there has been a tremendous amount of research intoimproving the manufacturability of materials into final products usingAdditive Manufacturing (AM). The main advantages of additivemanufacturing are the minimizing of waste material, as it is an additiveprocess, as well as the ability to create custom, low-volume, productswithout the need for creation of expensive tooling or programming beforethe manufacturing process begins.

Because of these advantages, however, AM is susceptible to uniquechallenges in the quality side of manufacturing. These challengesinclude minimizing and detecting defects during the build of the parts.

The initial focus of most methods in the literature address processmonitoring, in addition to mechanical and thermal property control,which is critical to ensure the process is optimized, thus increasingthe percentage of successful builds. In AM, as with other manufacturingprocesses however, there are possibilities of uncontrolled parameters,such as voids, foreign inclusions and lack of fusion between layers ortracks that can cause internal defects in the parts themselves. Suchdefects could lead to decreased mechanical properties and possible partfailure. Additionally, there may be systematic error due topeculiarities of the geometry of the control algorithms that are notreadily detected, due to the low volume AM production. Therefore,nondestructive methods for detecting defects are crucial for maximizingquality control in additively manufactured parts. With the improvementsof process monitoring and control, research has shifted tonondestructive testing and defect detection.

Whether it is waste, failed builds or part failure in the field due todefects, as additive manufacturing continues to expand into themanufacturing industry the need for quality inspection of these buildsmust expand as well.

Accordingly, what is needed in the art is a system and method fornondestructive testing and online process monitoring of AdditiveManufacturing (AM) processes that assures product quality.

SUMMARY OF INVENTION

In various embodiments, the present invention provides a non-destructiveevaluation (NDE) system and method based on Pulsed Thermography (PT) forthe detection of surface characteristics and defects in a 3D printedpart made from a thermoplastic material, such as acrylonitrile butadienestyrene (ABS). Due to the process speed and surface sensitivity, PT canbe integrated into a 3D printing system to permit layer-by-layerinspection, without drastically increasing overall build times.Integration of the PT defect detection mechanism allows for onlineprocess monitoring of each layer, thereby adding the ability to logdefects and make printing corrections, in-situ. This additional processcontrol can ultimately minimize the number of defects within a finalstructure and improve the quality and reliability of the printed parts.

In one embodiment, the present invention provides a method formonitoring a layer-based manufacturing process, which includes, exposinga surface layer of a 3D part undergoing a layer-based manufacturing to athermal energy pulse from a thermal energy source, the thermal energypulse having a pulse duration that is a function of one or moreproperties of a material of the 3D part, measuring a surface temperatureof the surface layer of the part with an infrared (IR) camera, inresponse to absorption of the thermal energy pulse into the 3D part, toidentify a location of a subsurface defect in the 3D part andcalculating a depth of the subsurface defect in the 3D part.

In one embodiment, calculating a depth of the subsurface defect in the3D part may be accomplished using a peak temperature contrast slopemethod. In another embodiment, calculating a depth of the subsurfacedefect in the 3D may be accomplished using a log second derivativemethod.

In the peak temperature contrast slope method for calculating a depth ofthe subsurface defect, the method may include, measuring a surfacetemperature above a defect-free area of the 3D part, measuring a surfacetemperature above a defect area of the 3D part, determining a peaktemperature contrast slope time based upon the surface temperature abovea defect-free area and the surface temperature above a subsurfacedefect. and wherein calculating a depth of the subsurface defect in the3D part, further comprises calculating the depth of the subsurfacedefect based upon the peak temperature contrast slope time, wherein thepeak temperature contrast slope time is directly proportional to thesquare of the depth of the subsurface defect.

In various embodiments, the pulse duration may be a range of pulsedurations and the range may be between about 100 ms and about 400 ms.Additionally, the pulse duration may be based upon a thermal diffusivityof the part and a depth of the subsurface defect being detected.

In an additional embodiment, the method of the present invention mayinclude detecting surface defects on the 3D part, layer-by-layer, duringthe layer-based manufacturing process. In this embodiment, the methodmay include, orienting the thermal energy source and the IR camera tominimize specular reflections from a defect-free (smooth) surface areaof the 3D part from reaching the IR camera, in response to the thermalenergy pulse, blocking the thermal energy pulse from the part followingexposing the surface layer of a 3D part undergoing additivemanufacturing to the thermal energy pulse and detecting specularreflections from a layer of the 3D part at the IR camera to identify alocation of a surface defect on the layer of the 3D part.

The methods of the present invention may be used in various layer-basedmanufacturing processes, including, but not limited to, Fused DepositionModeling (FDM), Powder Bed Fusion (PBF) and Binder Jetting (BJ). Theymay also be applicable to defect detection in processes that deposit asingle layer of material.

In another embodiment, the method may further include, calculating athermal diffusivity of the part based upon one or more detectedsubsurface defects.

The present invention additionally provides for a system for monitoringlayer-based manufacturing process, which includes, one or more thermalenergy sources positioned to expose a surface layer of a 3D partundergoing layer-based manufacturing to a thermal energy pulse, thethermal energy pulse having a pulse duration that is a function of oneor more properties of a material of the 3D part. In a particularembodiment, the one or more thermal energy sources are halogen bulbs.The system further includes, at least one infrared (IR) camerapositioned to measuring a surface temperature of the surface layer ofthe part, in response to absorption of the thermal energy pulse into the3D part, to identify a location of a subsurface defect in the 3D partand a processor for calculating a depth of the subsurface defect in the3D part.

The IR camera may additionally be properly oriented to sense thermalreflections from the surface layer of the 3D part to detect surfacedefects on the 3D part. The processor may further be used forcalculating a thermal diffusivity of the part based upon one or morereference features of known location in the part.

Accordingly, the present invention provides a non-contact system andmethod for detecting surface and subsurface defects in layer-basedmanufactured thermoplastic part. As such, the present invention providesan improved system and method for detecting defects in 3D-printed partsduring the manufacturing process.

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the invention, reference should be made tothe following detailed description, taken in connection with theaccompanying drawings, in which:

FIG. 1A is an illustration of the surface temperature decay curve of asound (defect free) area of the part versus an area with a subsurfacedefect.

FIG. 1B is an illustration of the temperature difference between thedefect area and the sound area of the part.

FIG. 2A is an illustration of temperature over time in the logarithmicscale of an area with a sub-surface defect and a sound area.

FIG. 2B is an illustration of the second derivative of the surfacetemperature decay where a defect is present in the log scale.

FIG. 3 is a schematic representation of time measurements in defectdepth calculation, wherein the peak slope is calculated from apolynomial fit of the data after the end of the pulse, but the peakslope time (ts) used for defect depth calculation includes half thepulse length (tp/2).

FIG. 4A is a 3D schematic representation of the simulation boundaryconditions used for 1D simulation of pulse thermographic method.

FIG. 4B is a 1D model to represent a defect region of the 3D model inFIG. 4A.

FIG. 4C is a 2D model to represent a defect free region of the 3D modelin FIG. 4A.

FIG. 5 is a graphical illustration of comparison of defect depthcalculations in simulation and experimental results for a range ofmaterials.

FIG. 6 is a graphical illustration of the experimental evaluations ofthe constants required to accurately calculate the exact defect depthsof the ABS and PLA parts. The black line is the least squares fit of theexperimental results.

FIG. 7A is a schematic diagram of the experimental setup for pulsethermography testing, in accordance with an embodiment of the presentinvention.

FIG. 7B is a diagram of the ABS printed part used for testing defectdepth calculations, in accordance with an embodiment of the presentinvention.

FIG. 8A is a table of the boundary conditions for simulation of ABS, inaccordance with an embodiment of the present invention.

FIG. 8B is an illustration of the calculated depths for a 0.5 mm defectdepth using the three peak slope time values with different startingtimes (to), in accordance with an embodiment of the present invention.

FIG. 9 is an illustration of thermal images over time of FDM printed ABSpart, in accordance with an embodiment of the present invention. NOTE:The cool spot in the top left corner is a cutout that was made for depthanalysis and not considered a defect for analysis purposes.

FIG. 10A is an illustration of a temperature contrast plotted over timefor each defect depth, in accordance with an embodiment of the presentinvention.

FIG. 10B is an illustration of the first derivative of the polynomialfit of the temperature contrast data for peak slope time calculations,in accordance with an embodiment of the present invention.

FIG. 11A is an illustration of the results for calculated defect depthsusing the peak temperature contrast derivative method, in accordancewith an embodiment of the present invention.

FIG. 11B is an illustration of the results using the log secondderivative method, in accordance with an embodiment of the presentinvention.

FIG. 12A is an illustration of specular reflections off a sound areacompared to specular reflections from a surface defect showing whysurface defect reflections show up as hotspots in the IR image, inaccordance with an embodiment of the present invention.

FIG. 12B is an illustration of the comparison between the surfacetemperature of sound areas to that of a defective area during and afterpulse heating of the ABS part, in accordance with an embodiment of thepresent invention.

FIG. 13A is a comparison of an ABS part after being pulse heated,wherein the image shows the surface reflections and emission of the partduring the pulse and image, in accordance with an embodiment of thepresent invention.

FIG. 13B is a comparison of an ABS part after being pulse heated,wherein the image shows the surface temperature emission after the pulsehas completed heating the part and the source shutter is closed toeliminate reflected IR light, in accordance with an embodiment of thepresent invention.

FIG. 14A is an illustration of the setup for the analysis of the surfacereflections from the PT method, in accordance with an embodiment of thepresent invention.

FIG. 14B illustrates an ABS printed Makerfarm part being analyzed forsurface defects, in accordance with an embodiment of the presentinvention.

FIG. 15A is an IR image of a 3D printed part at zero degrees startingpoint roads perpendicular to the heat source, in accordance with anembodiment of the present invention.

FIG. 15B is an IR image of the part at 90 degrees, roads are parallelwith heat source, in accordance with an embodiment of the presentinvention.

FIG. 15C is an IR image of the part at 180 degrees rotation with roadsperpendicular in opposite direction as starting point, in accordancewith an embodiment of the present invention.

FIG. 16 illustrates optical profilometry data of a portion of thesurface of the 3D printed part comparing surface roughness parallel withthe roads and perpendicular with the roads, in accordance with anembodiment of the present invention.

FIG. 17 illustrates the thermal image with reflections during theinitial pulse of the ABS part, in accordance with an embodiment of thepresent invention.

FIG. 18A illustrates a full-size image of the ABS printed part withmeasured surface defects, in accordance with an embodiment of thepresent invention.

FIG. 18B illustrates a magnified 44× picture of a portion of the ABSprinted part with smaller measured defect size for comparison, inaccordance with an embodiment of the present invention.

FIG. 19A illustrates a zoomed in IR image of the 3D printed partmatching the dimensions of FIG. 15B, in accordance with an embodiment ofthe present invention.

FIG. 19B is an illustration of the optical profilometry data of thesurface of the 3D printed part, in accordance with an embodiment of thepresent invention.

FIG. 20 is an IR image of nScrypt 3D printed part, in accordance with anembodiment of the present invention.

FIG. 21 is an optical image of the nScrypt 3D printed part in FIG. 16,showing an under extrusion between roads exposing the previous layer, inaccordance with an embodiment of the present invention.

FIG. 22 is a schematic representation of the measurement process fordensity of raw powder, in accordance with an embodiment of the presentinvention.

FIG. 23 is a schematic representation of thermal diffusivity measurementprocess, in accordance with an embodiment of the present invention.

FIG. 24 is an illustration of a Binder Jet (BJ) part to be used forthermal diffusivity testing of the material with different curingtemperatures, in accordance with an embodiment of the present invention.

FIG. 25 is an illustration of a fractional packing density comparisonbetween apparent and tapped density of 420 SS powder, in accordance withan embodiment of the present invention.

FIG. 26 is an illustration of a thermal diffusivity comparison betweenapparent and tap density of raw 420 SS powder, in accordance with anembodiment of the present invention.

FIG. 27 is an illustration of a thermal diffusivity comparison betweentwo binder jet parts, one cured at 165° C. and the other at 185° C., inaccordance with an embodiment of the present invention.

FIG. 28 is an illustration of a comparison of thermal diffusivitybetween raw powder and cured green parts, in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In various embodiments, the present invention provides a system andmethod for layer-by-layer quality assessment of an AdditiveManufacturing (AM) process, such as components manufactured by FusedDeposition Modeling (FDM). The present invention provides anondestructive testing method, utilizing a longer than typical pulselength, to assess additively manufactured parts for surface andsubsurface defect detection as well as thermal property determinationbased on a known void depth.

Various additive manufacturing processes are known in the art, includingPowder Bed Fusion (PBF), Binder Jetting (BJ) and Fused DepositionModeling (FDM).

Powder Bed Fusion (PBF) is a type of additive manufacturing processesthat fuses raw powder together layer-by-layer within a bed of powder toform a processed part. Different thermal sources including electronbeams and lasers can be used to fuse the material, though the laser ismost common. When a laser source is used, PBF is referred to as LaserSintering (LS). In LS, powder is spread, layer-by-layer, on the bedeither by a blade or a counter-clockwise rotating cylinder. The layerthickness is typically 100 μm. Upon completion of a newly spread layer,the powder is preheated and then the laser heats the layer to thedimensions of the specified cross section of the part. This processcontinues until a 3Dimensional part is formed. The types of materialsthat can be used is LS include plastics, ceramic, metal and glasspowder. The benefits of LS are that there is no post curing of thepowder required for proper strength and many parts can be built in asingle build. An example of laser sintered parts being studied for finalproduct use are air cooled heat exchangers for power plants.

Similar to PBF, Binder Jetting (BJ) utilizes a bed of powder to buildthe final part, however, instead of fusing the powder with a thermalsource, BJ uses a binder to adhere the powder particles together. In BJ,a layer of powder is spread across the bed and then the print head dropsbinder droplets, approximately 80 μm in diameter, onto the part in theshape of the cross-section of the part being built. Besides the adhesionmethod between particles, BJ is also different in the fact that thefreshly bound final part (green part) is quite fragile. Some bindersrequire thermal post processing to achieve adequate handling strength.Once the binder is set, the part is removed from the powder bed and postprocessing can begin to increase the part strength or mechanicalproperties to the final desired specifications. Post processing istypically accomplished by infiltrating the part with a lower meltingpoint infiltrant. For most steel powders, the infiltrant is bronze. Forother material systems, epoxy and cyanoacrylate have been used. The mostcommon material used in BJ is metal powder, though a notable applicationin the automotive industry is the use of BJ to make sand molds and coresfor casting. Though post processing is most likely required, because thebinder is added to the powder to create the part and many jets can beused simultaneously to deposit the binder, it should be noted that theBJ process is very fast compared to LS processing.

Fused Deposition Modeling (FDM) is an extrusion based additivemanufacturing system. In FDM, a solid strand of material, the mostcommon being Acrylonitrile Butadiene Styrene (ABS) and Polylactic Acid(PLA), are fed into and melted in the extruder head. The melted materialis then forced through the extruder head onto the build platform. Theextruded material is commonly known as “roads”. The roads are laid in arastering pattern to create a single layer. Since FDM is done on a buildplate and the material is extruded onto the plate, either the plate orthe extruder head must move the appropriate layer height before theprocess of extruding the next layer can begin. This process continuesuntil the part is completely built. The benefit of FDM is the ability tocreate hollow, or cellularly structured parts with different infills.With powder-based processes, any hollow enclosure would be filled withraw powder. This is beneficial for reducing the mass of a part that maynot undergo major external forces. Unlike powder-based process however,once the filament is extruded in FDM, it becomes waste material if notutilized on the part. The amount of waste material can be substantial.

Quality control is an essential process in manufacturing to ensuredefect-free final products. For most large-volume manufacturingprocesses, destructive testing of a finite sample of products is aviable method for defect detection. Large production volumes allow forapplying statistical process control techniques and the cost ofdestructive testing is small, as it is limited to a small percentage ofthe parts. This approach is not effective for additive manufacturing.

The layer-by-layer process of AM allows it to excel in parts with lowvolume and/or complex shapes. However, the process creates a challengingenvironment for quality control. The complex geometries complicatequality assessment and the low quantities make destructive testing forquality control much more expensive. Also because of the customizablenature and point localized material introduction with AM, many moredefect types and locations are possible. In FDM for example there areover 35 factors that can influence geometrical accuracy set by theoperator alone, withholding variation from the process itself.

While quality may be assessed based on monitoring the processconditions, current control methods may be insufficient to guaranteethat the same parameter settings will consistently produce defect-freeparts. Variations from input parameters as well as uncontrolled processand post-process variables may lead to variations between builds. Thesevariations will affect all different types of AM technologies. Forexample, with powder-based processes, the powder particle size hasnormal variation. Thus, within each layer, density variation may occurdue to variation in particle size distribution at each point when thepowder layer is deposited.

Pulse Thermography (PT) applies a thermal pulse to the surface of a partand monitors the spatial variation in the surface temperature over time.Materials previously studied utilizing this method of defect detectioninclude glass fiber reinforced polymers (GFRP) and aluminum (Al), 316stainless steel, ceramic composites, and carbon fiber reinforcedpolymers (CFRP). In AM, PT could be applied for defect detection,layer-by-layer, or for subsurface defects after a couple layers havebeen laid. This in turn, creates the possibility for online repair if adefect is detected before completion, thereby reducing waste material.

In PT, a thermal energy pulse is applied to a small layer on the surfaceof the part. The surface temperature is then monitored with an infrared(IR) camera, as heat is dissipated via conduction into the structure ofthe part. The dissipation of the heat leads to a surface temperaturedecay over time and is expressed as:

$\begin{matrix}{{T(t)} = {\frac{Q}{\rho\;{CL}}\left\lbrack {1 + {2\;{\sum\limits_{n = 1}^{\infty}{\exp\left( {{- \frac{n^{2}\pi^{2}}{L^{2}}}\alpha\; t} \right)}}}} \right\rbrack}} & (1)\end{matrix}$

where Q is the total input energy, ρ is the density of the part, C isthe specific heat capacity of the material, α is the thermal diffusivityof the material and L is the thickness of the part being analyzed. Theequation was derived with three major assumptions: no heat loss fromsurfaces of the part the thermal input is an instantaneous pulse (DiracPulse), and thus, there is a negligible internal temperaturedistribution after the pulse.

From this equation, experimental methods have been researched for thedetermination of the depth of the defect. A temperature contrast methodis known for defect detection which compares the difference intemperature from a defective area, and a sound (defect free) area on thepart being tested. In this method, the peak contrast temperature isproportional to the square of the defect depth. Stemming from thetemperature contrast method, it has also been found that the derivativeof the temperature contrast produces a peak that is proportional to thedefect depth. The method is known as the peak contrast derivative methodand the peak is present where the temperature change is greatest.

In various embodiments of the present invention, a peak contrastderivative time method is used to detect defects in a 3D manufacturedpart utilizing a long thermal energy pulse (>100 ms). The use of a longpulse serves to break down the key assumption of equation. (1), becausethe temperature distribution after the long pulse can no longer beapproximated as a pure surface heating. Thus, a temperature profilewithin the part is assumed and the temperature profile is dependent uponthe pulse duration and thermal properties of the part. Since the peakcontrast derivative time method uses a comparison with a referencedefect-free area on the part, the long pulse effects are minimized.

In PT, a pulse of energy is emitted onto the part across the frontsurface. Immediately following the pulse, thermal energy waves willpropagate one-dimensionally down perpendicular to the surface, viaconduction. This one-dimensional (1D) temperature distribution willcontinue through a defect-free part. However, when a large enough defectis present, the 1D conduction breaks down and 3D conduction begins tooccur. The 3D conduction slows down the conduction process and creates a“hotspot” on the surface of the part above the location of the defect.Utilizing a peak contrast derivative time method, the temperaturedifference between a defect location and a reference defect-free area onthe part is calculated. Calculation of the defect depth with this methodrequires a reference temperature distribution. If a reference area isnot known then one must be determined or a different method must beused. It is known in the art to use the average temperature over theentire surface as the reference temperature. This approach works forrelatively uniform surfaces with low levels of defects. Most defectsgenerate a low thermal conductivity region where material is missing, orwhere bonding between layers is lost as in a delamination. When a defectis present within a part, the 3D conduction path around the defect slowsheat transport from the surface and a change in the surface temperaturedecay is observed, as shown in FIG. 1A. This produces a temperaturecontrast over time, as shown in FIG. 1B, where temperature contrast isdefined as the difference between the surface temperature over a defectcompared to a sound region. The time the peak slope of this temperaturecontrast curve occurs is directly proportional to the square of thedefect depth. The correlation between the defect depth and the peakslope time (t_(s)) is expressed as:

$\begin{matrix}{t_{s} = \frac{3.64\; L^{2}}{\pi^{2}\alpha}} & (2)\end{matrix}$

where L is the defect depth. This method requires a reference sound areato calculate the temperature contrast and the reference soundtemperature is taken as the average over the entire surface. This methodworks if the defect area is a small percentage of the total surface areaof the part and the heating is uniform.

When the temperature and time is plotted in the logarithmic scale theideal temperature decay curve is linear, with a slope of −0.5 asexpressed as

$\begin{matrix}{{\ln\left( {T(t)} \right)} = {{\ln\left( \frac{Q}{\sqrt{\pi\;\rho\; C\;\alpha}} \right)} - {\frac{1}{2}{\ln(t)}}}} & (3)\end{matrix}$

When a defect is present, the temperature in the log scale will deviatefrom the linear trend as seen in FIG. 2A. The second derivative of thelog temperature of the defective region will produce a peak, as shown inFIG. 2B. The time where this peak occurs is proportional to the squareof the defect depth. The equation for determining the defect depth fromthe peak second derivative time (t₂) is expressed as.

$\begin{matrix}{t_{2} = \frac{L^{2}}{\pi\;\alpha}} & (4)\end{matrix}$

Unlike the peak temperature contrast slope method, the log secondderivative method does not require a reference sound area fordetermination of defect depth.

A non-dimensional measurement of the pulse length (τ) can be obtained bydividing the pulse length (t_(p)) by the (t_(s)) and the (t₂) values forthe peak temperature contrast and log second derivative methods,respectively.

For the peak temperature contrast method, the measured value of peaktemperature can be used or equation (3) can be substituted to makepredictions based on defect depths of interest, resulting in thefollowing equation for the peak temperature contrast method:

$\begin{matrix}{\tau_{s} = {\frac{t_{p}}{t_{s}} = \frac{\pi^{2}\alpha\; t_{p}}{3.64\; L^{2}}}} & (5)\end{matrix}$

Wherein, τ_(s)<0.7 is preferred, τ_(s)<1 is feasible, allowing a modesterror impact, τ_(s)<1.5 is acceptable with some corrections to the depthformula and τ_(s)<2 is the absolute theoretical maximum for this depthcalculation method.

For the log second derivative method, the measured value of the peaktemperature can be used, or equation (4) can be substituted to makepredictions based on defect depths of interest, resulting in thefollowing equation for the log second derivative method:

$\begin{matrix}{\tau_{2} = {\frac{t_{p}}{t_{2}} = \frac{{\pi\alpha}\; t_{p}}{L^{2}}}} & (6)\end{matrix}$

Wherein, τ₂<0.7 is preferred for negligible error impact, τ₂<1 isacceptable, allowing a modest error impact, τ₂<1.5 is feasible with someadjustments to the depth formula and τ₂<2 is the absolute theoreticalmaximum for this depth calculation method.

Literature reports of these methods have focused on pulse lengths of2-10 ms that closely approximate the instantaneous pulse assumption. Formaterials such as steel, these short pulses are required to accuratelydetect the peak slope. The pulse length required to approximate theinstantaneous pulse assumption will depend on both the diffusivity ofthe material and the depth of the defect. Since thermal diffusivity ofABS and other thermoplastics (α≈1.2×10⁻⁷) is much smaller than for steel(α≈4×10⁻⁶), a longer pulse is possible. The time difference is in factproportional to the thermal diffusivity differences. Pulse length may beincreased further by relaxing the assumption of negligible internaltemperature distribution.

Longer input pulses provide several measurement benefits. For example, alonger pulse allows a larger energy input into the system without havingto change the power source or lamp. Higher energy dramatically increasesthe temperature change, which reduces the sensitivity to measurementnoise. With a 4 kW power source, the energy input is 50 times largerwith a 100 ms pulse than it would be a 2 ms pulse, thereby producingapproximately a 50 times larger surface temperature contrast for thesame material. For materials with large thermal diffusivities or shallowdefects this may not be as critical, as the temperature signal is largeenough to overcome thermal noise. However, for materials with smallthermal diffusivities and when analyzing deeper defects, the ability tosimply adjust the pulse time to increase the energy allowing for alarger temperature signal is quite appealing. Additionally, longerpulses can be achieved with standard halogen lighting without theexpense of a high-power pulsed voltage source.

The ability to use a longer pulse is especially attractive innontraditional applications for low thermal diffusivity materials suchas polymers and powders. These applications are of particular interestin the Additive Manufacturing (AM) of metal components via powderprocesses because thermal conductivity of powder beds is significantlysmaller than the thermal conductivity of their bulk materialcounterpart. Thus, the thermal diffusivities are significantly smallerand modified testing parameters/equipment would be required for studyingpowder bed and green components produced by processes such as binderjetting. These techniques would also be appropriate for studying othergreen powder parts produced by traditional metal and ceramic powdertechniques and for polymer AM components. The longer pulse would behelpful for defect detection and improved accuracy.

FIG. 3 illustrates how equation (2) can be analyzed, with the startingpoint for the peak slope time determination at half the pulse length. Asshown in FIG. 3, the peak temperature contrast time for the defect(t_(d)) is found by fitting a polynomial to the temperature contraststarting at the end of the pulse. The (t_(d)) is added to half of thepulse length to find (t_(s)), which is the actual peak slope time(t_(s)) use for calculation of the defect depth. The updated equationcan be seen as:

$\begin{matrix}{{t_{d} + {\frac{1}{2}\; t_{p}}} = {t_{s} = \frac{3.64\; L^{2}}{\pi^{2}\alpha}}} & (7)\end{matrix}$

When the pulse length is small, relative to the peak slope time(t_(p)<<t_(s)), the original equation for quantifying defect depths viapeak slope contrast time is recovered.

In an exemplary embodiment, the method of Pulse Thermography (PT) isbased on 1D heat conduction into the part from the surface of the part.FIG. 4A illustrates a 3D part of interest. FIG. 4B illustrates a 1Dmodel showing a defect region and FIG. 4C illustrates a 1D model showinga sound region. Simulations based upon FIGS. 4B and 4C were performed toinclude both during the pulse and for a period of time after the pulsethat is sufficient to extract the significant information. The specifictime after the pulse varied based upon the thermal properties of thematerial beings studied. Upon calculation of the internal temperaturedistribution over time, predicted surface temperature over time wasextracted for both the defective region simulation (FIG. 4B) and thesound region simulation (FIG. 4C).

The defective region has a thickness equal to the defect depth (L_(d) inFIG. 4B) while the sound region is modeled with a thickness greater thantwice the thickness of the defect (L_(s) in FIG. 4C). The sound regionneeds to be at a minimum of twice the width of the defect depth forequation (2) to accurately calculate defect depth with the constant3.64. If the depth of the sound region is not more than twice the depthof the defect region being measured, then equation (2) will notaccurately calculate the depth and the constant of 3.64 will need to bechanged as discussed above.

In this exemplary embodiment, the sides were set as insulated boundaryconditions, based on the 1D conduction analysis, and the heat flux was aunit step function. The magnitude of the heat power was held constantwhile the pulse length was varied, thus varying the energy input. Theheat flux applied during each pulse length was set at 4000 W. Theemissivity value of 0.9 was chosen to increase the effect of radiativeheat loss as well as to better represent practical experimentationcases, as most low emissivity materials are coated with a thin layer ofhigh absorptivity paint for testing purposes.

An assumption that neglects all heat loses is acceptable when themeasurements are taken in a short time period to ensure that very littlecooling takes place before the features of the defect are observed inthe surface temperatures. However, with deeper defects or small thermaldiffusivities, the peak times occur much further from the end of thepulse. As the cooling time increases, there is an increased possibilitythat the thermal losses will affect the measurements. To understand theeffect that heat losses have on the defect depth calculation,simulations were run with and without convective and radiated heatlosses on both surfaces of the part and with the defect with parametersselected to be representative of testing in an environment with auniform initial temperature.

The Forward Time Center Space (FTCS) method was used to perform the 1Dnumerical solution. The FTCS is an approximation method derived from the1D heat equation:

$\begin{matrix}{{\frac{\partial T}{\partial t} = {\alpha\frac{\partial^{2}T}{\partial t^{2}}}},{0 < x < L},{t \geq 0}} & (8)\end{matrix}$

where α is the thermal diffusivity of the material and L is thethickness of the part. For a stable solution the value of r is given as:

$\begin{matrix}{r = {\frac{\alpha\;\Delta\; t}{\Delta\; x^{2}} < \frac{1}{2}}} & (9)\end{matrix}$

must be less than the constant 0.5. For the simulations, the timediscretization was varied based on the material being simulated toassure convergence. The simulation was implemented in MATLAB, with thedefect depth calculation determined by the peak temperature contrastmethod. Solutions were verified against a commercial thermal solver,with the same boundary conditions. There were minimal differencesbetween the two methods, which were attributed to different time andspatial discretization between the two approaches. To more accuratelyrepresent application of the peak slope time defect depth quantificationto experimental data, a normally distributed random noise with astandard deviation of 0.0014K was added to the simulation data. The wasobserved to have an impact on the minimum energy input required to solvefor the defect depth, but if input energy in increased, there is not asignificant impact of the noise on the calculated depth values.

In an experimental analysis of the pulse duration, an FLIR SC4000 MWIRinfrared camera (3-5 μm sensitivity) was used to analyze the surfacetemperature with a 50 mm indium antimonide (InSb) lens. The frame ratewas set at 100 hz with a focal plane array of 320×256 pixels. For pulseheating of the surface, two 500 W halogen bulbs were set approximately7″ from the surface of the part with an incidence angle of less than 25.Two pieces of acrylic (PMMA) glass was used to filter infrared light toavoid temperature errors from reflections into the IR camera. The noisein the temperature data of the camera was measured at ˜0.007° C. whenspatially averaging the acquired temperature over an 8×8 mm area.

TABLE I Material Properties of the Four Materials Used in the DefectDepth Simulation Thermal Specific Thermal Conductivity, HeatDiffusivity, K Capacity, C Density, α Material (Wm⁻¹K⁻¹) (Jkg⁻¹K⁻¹) ρ(m²s⁻¹) ABS 0.2256 1386 1020 1.596 × 10⁻⁷ PLA 0.13 1800 1300 5.556 ×10⁻⁸ Copper 400 398 8912 1.128 × 10⁻⁴ 316 SS 16.2 500 7990 4.055 × 10⁻⁶

Table I shows the material properties of the materials used in thesimulation. For the experimental evaluation, the thermal diffusivity ofAcrylonitrile butadiene styrene (ABS) (1.137e-07 m² s⁻¹) and Polylacticacid (PLA) (1.414e-07 m² s⁻¹) were used. These thermal diffusivityvalues were previously found experimentally using a long pulsethermography method with pulse lengths of less than 500 ms. Two defectdepths were analyzed for both ABS and PLA. For ABS, the defect depthswere 0.85 mm and 1.15 mm and the defect widths for each were 8×8 mm. Forthe PLA analysis, the two defect depths were 0.68 mm and 1.0 mm with awidth of 12×12 mm. Each part was flashed with pulse lengths ranging from0.25 s up to 5.0 s. This range was chosen based on the measured valuesof thermal diffusivity for each material and their defect depths.

To acquire the peak slope times, a polynomial fit of the temperaturecontrast was performed to minimize the effect of the noise when takingthe derivative as is commonly done in practice. The zero-time point wastaken as the midpoint of the pulse. The equation for the polynomialcurve was then used to determine the derivative of the temperaturecontrast curve and the peak slope time was acquired from this derivativeto find t_(d) which is used to calculate t_(s) using equation (7).

The simulation results showed that heat loss from the surfaces(emissivity, convection) had negligible impact. The heat losses impactedcalculated depths in the metals less than 1%. While the lower thermaldiffusivity, and thus longer times before the peak slope occurs,increased errors in polymers, the heat losses still only changed ABS andPLA depth calculations by 1-2%. This variation is much less than thepossible variation and error from noise and fitting of the temperaturedata. While the difference was negligible, the simulations with heatloss were used in all the following results.

The simulation error was calculated by comparing the defect depthcalculated from the simulation data to the actual defect depth. Theerror was analyzed as a function of the normalized pulse lengths definedin equation 5 and equation 6. As seen in FIG. 3, the error in the defectdepth calculation is well correlated to the normalized pulse length forboth analysis methods and for both polymer and metals.

FIG. 3 shows that for any given defect depth, the error in the defectcalculation should stay below 5% if t_(p)/t_(s)<1. Under theseconditions, the pulse length does not exceed the actual peak slopecontrast time of a defect. For example, ABS with a 0.5 mm sub-surfacedefect, has a peak slope contrast time (t_(s)) of 577 ms. Thus, themaximum pulse length to accurately calculate this defect within 5% erroris approximately equal to 577 ms. The experimental long pulse resultsshow the same trend of increasing error with longer pulse lengths as thesimulations. In both cases, the error in the calculated depths trendsupwards once the pulse length reaches approximately 70% of the actualpeak slope contrast time for the material. Table II illustrates themaximum pulse length to obtain accurate defect detection within 5% errorbased on the simulation results for several materials and depths.

TABLE II Predicted Maximum Pulse Lengths for Less than 5% Error forVarying Materials and Defect Depths Defect Depth ABS PLA 316 SS Copper(mm) Peak Slope Peak Slope Peak Slope Peak Slope 0.3 0.208 s 0.597 s0.0082 s 0.0003 s 0.5 0.578 s 1.66 s 0.023 s 0.0008 s 1.0 2.31 s 6.63 s0.091 s 0.0033 s 2.0 9.24 s 26.55 s 0.36 s 0.013 s

The experimental results of ABS and PLA confirm the general trend of thepercent error for defect depth calculation compared to the simulatedcalculations as seen in FIG. 3. At approximately 75% of thecharacteristic peak slope times for each material and for each depth,the percent error in defect depth calculation begins to increase. As thenormalized pulse length increases, the error in calculated depthincreases but is only 15% at t_(p)/t_(s)=2.

Given the consistency of the error variation with pulse length, acorrection factor can be applied. In this case, when the normalizedpulse length exceeds approximately 0.75, the error increases nearlylinearly. Beyond the normalized pulse length of approximately 0.75, theconstant of 3.64 in equation (7) will need to be adjusted. The necessaryadjustments to the constant can be seen in FIG. 5. Using the t_(d) fromthe experimental results of the ABS and PLA, the constants required toexactly calculate the depth were plotted in relation to t_(p)/t_(s). Abest fit of the data was applied and the best fit line should be used toaccurately calculate the defect depth when t_(p) is greater thanapproximately 75% of t_(s). However, since even when t_(p)/t_(s) isequal to 1, the experimental results showed accuracy within 5%, therecommendation to re-evaluate the constant in equation (7) can beextended to t_(p)/t_(s) ratios larger than 1.

FIG. 6 shows the experimental and simulation results, with the constantin equation (7) re-evaluated based on the respective normalized pulselength ratios for each trial. It can be seen that, by re-evaluating theconstants based on the normalized pulse length greater than 0.75 thecalculated defect depths percent error dropped to within approximately5% of the actual depth across the entire range of experiments andsimulation results.

Even after adjusting the constant based on the normalized pulse length,the method fails as t_(p)/t_(s)→2. The peak slope in the temperaturecontrast disappears after the normalized pulse length exceedsapproximately 2. As the pulse length increases for a constant defectdepth, the time the peak slope occurs after the pulse (t_(d)) willdecrease to zero at t_(p)/t_(s)=2. Beyond this point, the continuedheating alters the relationship and even before this limit, a short gapbetween the end of the pulse and the peak slope time complicatesidentification of the peak. Thus, a normalized pulse length of 2 is themaximum for accurately quantifying defect depths with a longer pulseusing standard pulse thermography methods.

In comparing the experimental results to the simulation results, asshown in FIG. 6, it can be seen that, the experimental results' percenterror trended lower than the simulation for the same pulse length. Also,with shorter pulse lengths the experimentally calculated depthsexperienced more variation between ABS and PLA. The variation may be dueto effects such as nonuniform input flux or variation in the thermalproperties due to small differences in the void content due to changesin the 3D printing processes used to create the parts. Even with 100%infill target, there will be a thermal resistance between each extrudedfilament and small voids. This could create a slightly lower thermaldiffusivity that would offset the results.

The ability to characterize the maximum allowable pulse length foraccurate defect detection is a crucial benefit in analyzing a specificpart. Based on the expected defect depths, a maximum pulse length can beselected that increases the energy input for higher signal to noiseratios. Comparing PLA versus ABS, two materials most commonly used forFDM printing, the thermal properties are different. As such, at 4 kWpower and a 2 ms pulse the thermal contrast at the point of peak slopefor ABS is 68% larger than that for PLA, but both are below the expectedexperimental noise level of the SC4000 camera (0.007° C. when averagedover a small region). Thus, either a longer pulse or more power toproduce a larger energy input is required to quantify the defect depth.

The aspect of increasing the power to achieve the necessary signal tonoise ratio however, can produce unintended thermal effects on the partbeing tested. For example, in order to put 400 J of energy into a partin 2 ms, the temperature increase of the surface would be approximately17° C. This level of temperature increase could introduce more error dueto possible phase changes and significantly added radiative heat losses.With a 320 ms pulse, the surface temperature doesn't exceed 2° C. but,the temperature contrast between the defective and sound region is thesame. Thus, to achieve the best signal to noise ratio and an increasepossibility of accuracy in quantifying defect depth, increasing thepulse length is the better of the two choices.

Acceptable pulse length is a function of the depth of defects beingexamined and the thermal properties of the material. Both the simulationand the experimental results showed that the maximum pulse lengthachievable for a given material to be within 5% error is equal to thepeak slope time for the specific depth that is being measured. Theexperimental results trend on the lower side of the calculated error,allowing for pulse lengths up to 1.2 times the peak slope time in thiscase. Given the consistency of the error across a range of materials,the depth formula could be adjusted when the normalized pulse lengthexceeds 0.75 to more accurately quantify the defect depth. By utilizingthe maximum allowable pulse length for accurate defect detection, theenergy input can be significantly increased creating larger thermalcontrasts on the surface. This in turn minimizes the possible error fromthe signal to noise ratio and also allows for defect depth measurementsof a much wider range of defect depths as well as a much wider range ofmaterials.

3D additive parts are commonly formed from one or more thermoplasticmaterials. The PT method previously described has been used to detectdefects in materials such as steels, ceramics and composites. However,these materials have higher thermal diffusivities (α) than athermoplastic, which accounts for shorter time periods before the defecttemperature gradient appears on the surface of the part, as evidenced byequation (2). The higher thermal diffusivity materials require a shorterpulse to simulate a Dirac heating pulse that is sufficient to identifythe peak contrast derivative time at the surface.

Due to the low thermal diffusivity of thermoplastics, such asAcrylonitrile butadiene styrene (ABS), the time difference between whenthe temperature profile reaches the defect and begins 3D conduction ismuch longer than with higher thermal diffusivity materials. Morespecifically, the time difference is proportional to the difference inthermal diffusivity. For example, ABS has an approximate thermaldiffusivity that is 1/25th that of 316 stainless steel (SS). Therefore,it takes 25 times longer for a temperature gradient from a defect to beseen at the surface in a part manufactured with ABS, in comparison to astainless-steel part. The time difference before the temperature profilereaches a defect and begins 3D conduction that is attributed to the ABSmaterial, allows for a longer pulse rate, thus eliminating the need forflash lamps.

Commonly in PT, two flash lamps of 2000 watts (W) are flashed for arange between 2-10 ms to produce a range of 8-40 joules (J) of energythat is projected onto the surface of the part being thermally excited.By simply increasing the flash duration form 2 to 10 ms (5×), the energyinput increases as well 5×. To be able to increase the amount of theenergy even more would require a larger capacitor to store more energy.

However, in the present invention, by using a steady continuous voltagesupply for longer pulses, the same energy input of 32 J can be producedwith two 500 W halogen bulbs flashed for 40 ms. And halogen bulbs can beflashed for a much longer duration, increasing the energy even more.

In an exemplary embodiment of the PT detection system of the presentinvention 100, instead of flash lamps as required for a 2-10 ms pulse,two 500 W halogen bulbs 105, 110 rated for 120V, 56 degrees fromincidence of the surface of the part 130, were flashed for 300 ms at 120V. After the pulse of heat is completed, two shutters 120, 125 arerotated into place, thereby blocking the halogen bulbs 105, 110 from thepart 130, as shown in FIG. 7A. Even after the pulse is completed, thehalogen bulbs 105, 110 emit radiant heat while they are cooling down.This radiant heat not only continues to input energy into the part 130but also reflects off the surface of the part. This reflectionintroduces error into the infrared temperature measurements made by theinfrared camera 115. In this exemplary embodiment, the infrared camera115 used for the experiments is an FLIR SC4000 MWIR reading infrared inthe midwave spectrum 3-5 μm with a 50 mm indium antimonide (InSb) lens.The frame rate was set at 60 hz with a focal plane array of 320×256pixels.

Following the completion of the pulse, the shutters 120, 125 wereengaged to block the radiant heat and the surface temperature of thepart 130 was monitored by the IR camera 115 for a total time of 15seconds. In this exemplary embodiment, a sample ABS part was printedusing FDM with intentional defects introduced. The defects used in thesecalculations were 8 mm×8 mm. The average temperature taken over thesurface area of the defect was used for calculation of the depths of thedefects. For the reference sound area, an average temperature was takenin the closest sound region to each defect depth with the same overallsurface area as the defect. A reference sound area was taken for eachdefect to minimize any error from spatial variation in heating intensityacross the surface.

Acrylonitrile butadiene styrene (ABS), one of the more common FDMprinted thermoplastic materials, was used for defect depth prediction inthis exemplary embodiment. A 50×80×8 mm rectangular part was printedwith four different defect depths, with three difference widths, foreach depth. The schematic of the printed part is shown in FIG. 7B. Tominimize reflectivity and increase absorptivity, the part was printedwith black ABS. The defect depths were approximately 0.3 mm, 0.8 mm, 1.2mm, and 1.8 mm. The layer patterning was set to standard and an infillpattern of 100% was used. Due to the variations in thermal propertiesthat can arise from the printing process, additives in the ABS material,and the AM process itself, the thermal diffusivity used was based on thebest fit of calculated depths. Each measurement was repeated multipletimes to assess the repeatability of the method.

From equation (2), the peak slope time is directly proportional to thesquare of the defect depth. As the pulse length increases, theinstantaneous heat input assumption will break down and ultimatelychange the calculated depth. To understand how this would affect thecalculations, a Solidworks® thermal simulation of a 0.5 mm deep defectwas analyzed with the boundary conditions shown in FIG. 8A. Due to thelonger peak slope times with a low thermal diffusivity material likeABS, heat losses consisting of radiation and convection will be includedin the simulation. Three different starting points were used for thepeak slope time calculation of the defect depth. The first value was thetotal time with to starting at the end of the pulse, the second valuewas the time with to starting at the beginning of the pulse, and thethird value for depth calculation was the time with to starting in themiddle of the pulse.

FIG. 8B shows the calculated depths from each of the peak slope timevalues. At short pulse times, all give the same results. However, whenstarting to at the end of the pulse, the calculated depths decrease withincreasing pulse length and when starting to at the beginning of thepulse the calculated depths linearly increase with increased pulselength. When using the peak slope time value with to starting at halfthe pulse length, the calculated depths become independent of pulselength over the range studied. Extending the pulse length allowed formore energy input which increases the temperature contrast to detectdeeper defects.

FIG. 9 illustrates the surface temperature, over time, with the 300 mspulse. After nine seconds of surface monitoring the three defect depthsof 0.3, 0.8, and 1.2 mm are visible in the thermal image. The 1.8 mmdefect is not visible in the thermal image at any time step, nor in thetemperature contrast data, as seen in FIG. 10A. Beyond 1.2 mm depth, anytemperature difference between sound and defect regions are within themeasurement noise. Presumably, more energy input is required formeasurement of the depth of 1.8 mm. Therefore, for the 1.8 mm defectdepth, pulse lengths of 1.5 s, 4 s, and 6 s were used for quantificationof defect depth.

In order to reduce noise, a small rectangular region of pixels wasaveraged at each time point for the sound and defect region. The defectdepths were calculated by taking a polynomial fit of the temperaturecontrast data, and then the first derivative of that, as seen in FIG.10B, was taken to find the peak slope times t_(s). Based on thesimulation results, t=0 is taken as the midpoint of the pulse and it canbe seen in FIG. 11A that the theory is confirmed and that even with alonger pulse, the defect depths can be calculated using the peaktemperature contrast slope method.

As the defects get deeper, the variation of the calculated depth getslarger, when using the peak temperature contrast slope method. That isas expected however, because the deeper defects create a much smallertemperature gradient as energy is dissipated over time. With thesesmaller temperature gradients, it becomes harder to differentiate fromnoise in the temperature measurement and the effects of substratedefects. Also, the deeper the defect, the larger cross-sectional arearequired for accurate depth calculation. The 8×8 mm width of the 1.8 mmdefect is not large enough for accurate prediction within ABS, which hasa low thermal diffusivity.

Beyond 1.2 mm, the calculated depths were consistently shallower thanthe actual depth. This may be due to the approximations in the quadratictime/depth relationship. However, with AM, this is not the area ofgreatest interest for subsurface defect detection. For online processmonitoring, defect depth quantification would be focused for only a fewlayers, to allow for the possibility of repair or scrap before buildcompletion. Deeper defects would be more of a qualitative analysis todetermine if a defect, such as delamination, is present in the part.

Using the log second derivative method, it can be seen in FIG. 11B, thatwith this experimental setup, the depth calculations have a much largervariation. This could be attributed to the internal temperaturedistribution from the longer pulse or compounding noise from taking thesecond derivative of the small temperature rise. Thus, for low thermaldiffusivity materials, like ABS, and the tested pulse conditions, thepeak temperature contrast method gives better results. As such, based onthese studies different pulse conditions, reduced measurement noise, orimproved post-processing is necessary for effective defect depthcalculation using the log second derivative method.

In the above described embodiment, the system and method of the presentinvention are effective in detecting and quantifying subsurface defectsafter the part is built or after a plurality of layers have been laid.By using a longer pulse, accurate defect detection becomes possible withvarious pulse lengths. Thus, allowing for the capability of increasingthe pulse length to increase the energy input into the part. Byutilizing the maximum allowable pulse length for accurate defectdetection, the energy input can be significantly increased creatinglarger thermal contrasts on the surface. This in turn minimizes thepossible error from the signal to noise ratio. It also allows for defectdepth measurements of a much wider range of defect depths as well as amuch wider range of parts with various thermal properties.

However, the pulse length cannot exceed approximately 80% of the actualpeak slope contrast time for that defect to stay within 5% error fordefect depth calculation. For example, ABS with a 0.5 mm subsurfacedefect, has a peak slope contrast time of 577 ms. Thus, the maximumpulse length to accurately calculate this defect within 5% error isapproximately 462 ms. Because of the varying thermal properties ofdifferent materials, this maximum pulse length will vary as well. TableIII shows for a given set of defect depths the maximum pulse length toobtain accurate defect detection within 5% error.

TABLE III Predicted maximum pulse lengths varying materials and defectdepths Defect ABS 316 L grade SS PLA Copper Depth Log Peak Log Peak LogPeak Log Peak (mm) Method Slope Method Slope Method Slope Method Slope0.3 0.144 s 0.166 s 0.0057 s 0.0065 s 0.413 s 0.478 s 0.0002 s 0.0002 s0.5 0.399 s 0.462 s 0.0157 s 0.0182 s 1.15 s 1.33 s 0.0006 s 0.0007 s1.0 1.6 s 1.85 s 0.0628 s 0.0728 s 4.58 s 5.31 s 0.0023 s 0.0026 s 2.06.38 s 7.40 s 0.251 s 0.291 s 18.33 s 21.24 s 0.009 s 0.0105 s

The standard pulse length for PT is 2-10 ms, for copper this would meanthe shallowest defect that could be quantified would be 1 mm. Yet forPLA, even at 0.3 mm the max peak using the log second derivative methodis 412 ms.

The ability to characterize the maximum allowable pulse length foraccurate defect detection is a crucial benefit in analyzing a specificpart. For the FDM process for example, the two most common materialsused are PLA and ABS. Yet for defect detection, if the currentexperimental setup is set to pulse the maximum pulse lengths for ABS,the temperature contrast might not be sufficient to differentiate fromthe noise if the material was switched to PLA. Thus, the pulse timeswould need to be modified. Modifying the pulse length based on thematerial being tested is very beneficial for signal to noise ratio.

Even with the capability to detect subsurface defects within a fewlayers, material and machine time are wasted because the defective areahas already been printed over with new layers. Therefore, the mosteffective form of defect detection would be on the surface, bymonitoring each layer as it is laid. This would allow for immediaterepair if a defect is detected. For this level of process monitoring,the present invention proposes the use of reflected light in theinfrared spectrum to characterize the surface and detect some types ofdefects before an additional layer is printed.

As such, in an additional embodiment, the present invention provides asystem and method effective in monitoring the surface of a part while itis being printed, thereby allowing for possible repair during themanufacturing process, if necessary.

As previously discussed, quantitative analysis of subsurface defectdepth focuses on the decay of surface temperature after the end of thepulse. The effectiveness of subsurface defect detection via pulsethermography depends on the size of the defect and the depth. As shownfrom the results, depths were accurately calculated up to 1.2 mm with an8×8 mm width defect. When it comes to mechanical properties however,defects much smaller than this can cause mechanical failure in the part.It is known that during tensile testing, some ABS parts fail prematurelybecause of small imperfections/defects including microcracks in thesurface. Thus, the ability to detect fine defects is critical for partperformance.

Distinguishing surface defects with an IR camera requires the defects tobe heated differently than the surrounding (defect free) areas of thepart. The surface roughness of 3D printed parts however, is very smallcompared to the distance of the heat source. Therefore, even largesurface defects have uniform temperature within the surrounding area.However, when using a modified PT method, with illumination in theinfrared, wavelengths measured by the camera and monitoring thetemperature during the illumination period, it has been found that whilethe bulb is heating the part, the radiant heat can reflect off thesurface features into the IR camera to create an immediate hotspot onthe surface. The highlighted defects are dependent on the relativeorientation of the source, defect edges, and camera.

There are two types of reflections that can occur from the surface ofthe part resulting from the radiant heat, including specular reflectionand diffuse reflections. Specular reflections occur when the angle ofthe incoming radiant heat source reflects off the face of the part atthe same angle of incidence and follows Fresnel equations. FIG. 12Aillustrates the specular reflections 230 off a sound (defect free) area210, compared to specular reflections 235 from a surface defect 205,showing why surface defect reflections 235 show up as hotspots in the IRcamera 215 image.

Diffuse reflections occur not along the same angle of incidence but in aspherical angular distribution from the plane of interference followingLambert's cosine law. Because no surface is perfectly smooth there willalways be diffuse reflections occurring from the surface of a part. Thediffuse reflections have a lower intensity than the specular reflectionsand can be ignored for the analysis of surface defect reflections seenin the IR image. The significance of the diffuse reflection intensitycompared to the radiant emission of the product is minimal. This can beseen in FIG. 12B as temperatures in the sound (defect-free) areasproduce only diffuse reflections in the IR image and show no significantdrop in temperature, once the radiant heat source is shielded from thepart. In comparison, defective areas produce specular reflections intothe IR image, thereby illustrating a significant temperature differenceuntil a shutter is placed in front of the heat source to block allradiant heat, following the application of the pulse.

Immediately following the blocking of the radiant heat source, thedefective area temperature drops down to the surrounding sound areas. Inan exemplary embodiment, FIG. 13A shows the IR image of the ABS 25×25×8mm part during pulse heating of the part. In this embodiment, the 3Dprinted part was pulse heated for 400 ms and after the completion of thepulse, shutters were placed in front of the heat source to block anyradiant heat emitting from the bulbs during cooling. The area of theappearing hotspots is not physically hotter after the 400 ms pulse thanthe surrounding area as shown in FIG. 13B, but because it has adifferent surface profile than the surrounding area it allows for aspecular reflection of the radiant heat to be directed into the IRcamera. Therefore, they are reflective spots, and by using the PT methodwith a longer pulse, a picture of the surface profile can be visuallyanalyzed.

In an exemplary embodiment for detecting surface defects of an ABS 3Dprinted parts, the detection system 300 is illustrated in FIG. 14A,which includes two 110V 500-watt Halogen lamps 305, 310. The part 330being analyzed was a 25 mm by 25 mm and 8 mm thick square made ofthermoplastic, ABS, and printed on a MakerFarm 8″ Prusa I3v printer. Thenozzle diameter was 0.4 mm and a layer height of 0.2 mm was selected.The part was rotated 90° so that the heat source was perpendicular andparallel to the filament. The camera 315 was set at an angle of 90° fromthe surface to minimize any specular reflections from a sound area andthe bulbs 305, 310 were set at 45° from the surface of the part 330. Thehalogen bulbs 305, 310 were pulsed for 400 milliseconds and then shutoff to allow time for analysis of the surface reflections. Aftercompletion of the pulse heating, shutters (not shown) were rotated infront of the halogen bulbs 305, 310 to block all radiant heat emitted bythe bulbs during cooling, thereby eliminating the reflections seenduring the pulse.

With a specular reflection, the reflected angle always equals theincident angle. Because of this, the IR camera 315 cannot be setup atthe same angle as the heat source to the part 330. That way the IRcamera does not pick up the specular reflections from flat planarsurfaces. For the camera 315 to pick up a specular reflection of radiantheat, the surface face must create a plane of incidence between the heatsource and the IR camera 315 that allows the reflected heat to be seen.This includes defects and road edges, as well as the faces of the roads.FIG. 15A illustrates the IR image of the 3D printed part at 0° startingpoint with roads perpendicular to the heat source. FIG. 15B illustratesthe IR image of the part at 90°, wherein the roads are parallel with theheat source. FIG. 15C illustrates the IR image of the part at 180°rotation, with the roads perpendicular, and in opposite direction as thestarting point. FIG. 15A-FIG. 15C show the effect of rotating the part180°, thereby allowing the heat source to reflect off the surface andthe roads at different angles. When the source of the heat isperpendicular to the road direction, the radiant heat reflects off thecurved faces of each road. This creates more reflective lines on thesurface, masking smaller defects. It does, however, give an approximaterepresentation of the road profiles and the relation to adjacent roads.Depending on the surface profile of a road, it will reflect differentlyinto the IR camera. If the road has a more curved profile, it increasesthe possibility of creating an angle of incidence into the IR camera.

FIG. 16 shows optical profilometry data comparing the surface roughness(in microns) between traveling perpendicular to the road direction vs.parallel to the road direction. The road heights vary by approximately20 microns and have a curved profile, correlating with the reflectivelines that appear in the IR image when the heat source is perpendicularto the roads. When the heat source is parallel to the filamentdirection, most of the road reflections will not be seen by the IRcamera unless there is a defect or if a portion of the surface of thepart is not flat. The profilometry correlates to this theory as theroughness along the roads is less than 5 microns, with no significantlycurved profile, thus producing only diffuse reflections into the IRcamera.

Thermal images were analyzed during the flash as seen in FIG. 17, withthe road direction parallel to the heat source. There are many surfacereflections that appear on the surface during the pulse. Most of surfacereflections follow a diagonal line from the bottom corner of the partgoing up to the top right of the part. This diagonal line appears to befrom the extruder tip being too close to the surface of the part when itchanges position from that spot, thereby pushing the edges of thefilament and creating a line of ABS. There are also a few smallreflective spots scattered throughout the surface of the part. Thebiggest reflection is in the lower left portion of the part, near thediagonal line.

When analyzed using a magnifying camera, the reflective zone is asurface hole defect approximately 1 mm in diameter as well as noticeableholes forming a line perpendicular to the filament direction, measuringapproximately 1.5 mm, as seen in FIG. 18A. FIG. 18B is a focused view ofthe area with both defects measured in FIG. 18A and showing defects thatcan be seen in the IR reflected image as small as 181 μm. The size ofthe defect that can be seen from the reflected radiation into the IRcamera depends upon the overall quality of the part surface. FIG. 19Ashows a comparison between a zoomed in IR image of a portion of the 3Dprinted part compared to the optical profilometry data for that section,as shown in FIG. 19B. The profilometry image and the IR image aresimilar, showing defects microns in diameter.

The focus, so far, of reflective lines has been on hole or high spotdefects, as well as the effect of the road direction relative to theheat source. There is another defect that can occur during printing,which is commonly referred to as “under extrusion”. Under extrusionoccurs when the filament is stretched from the nozzle, producing a roaddiameter that is smaller than the surrounding roads, thus leaving a gapbetween the roads. The 3D printed part that was analyzed for holedefects, in the prior discussed exemplary embodiment, did not have anyunder extrusions between the roads, therefore, in this exemplaryembodiment, a 3Dn-Tabletop nScrypt printed part fabricated with a nozzlediameter of 0.2 mm and a layer height of 0.1 mm, having a known underextrusion was analyzed.

In this exemplary embodiment, the ABS part was thermally pulsed with thesame experimental setup as the previously discussed part and FIG. 20shows the IR image of the 3D printed part in this exemplary embodiment.The part was pulsed with the heat source parallel with the roads and inthe IR image a clear vertical reflective line is visible, as well asother reflective spots throughout the surface. The two larger hotspotsin the bottom of the part are marker lines and not actual defects fromthe printing of the part.

The bright spots related to the under extrusion are reflections from theprevious laid layer. As noted earlier when the road direction isperpendicular to the heat source, the curved profile of the road'ssurface will reflect the radiant heat into the IR image. Since there isan under extrusion, this leaves the previous layer's roads exposedallowing them to reflect the radiant heat, thereby exposing the underextrusion. FIG. 21 shows a magnified optical image of the partcontaining the under extrusion. As you can see the roads that form theprevious layer are visible and capable of reflecting radiant heat intothe IR camera. There is also a smaller under extrusion two roads over tothe left, appearing in the optical image, but it is not as extreme anunder extrusion with a portion of the adjacent roads connecting.

Using Pulsed Thermography to be able to identify defects within a partafter the product has been built or multiple layers have been laid is astep in the right direction for quality control in the field of AM. Foronline integration of a monitoring system being able to use this newform of reflective thermography from a longer pulse time, will allow theradiant heat to reflect from the heat source to the IR camera, therebyadvancing the capability for quality control a step further. Defects,such as indentations, drag marks from the extruder nozzle and underextrusions can be identified by the reflections they produce, incomparison to the sound areas surrounding them. When the heat source isperpendicular to the road direction, an approximation of the roadprofiles can be made as well as the overall roughness of the part.

In additional embodiments, quantitative analysis methods between thesurface roughness and the reflection from the roads in the IR image canbe implemented. Currently, the profilometry data is only being used asvisual comparison analysis. It is important to note that the sensitivityof this method as a detection method of defects depends on the overallquality of the surface; the more larger defects the part has on thesurface the less sensitive the inspection is of smaller defects.Coupling the capability to monitor the surface profile with the abilityfor defect detection and quantification in sublayers using the PT methodwith a longer pulse creates multiple layers of online qualityinspection.

For effective implementation of both methods, the thermal source wouldneed to be parallel with the road direction during the build. Therefore,for the reflective thermography, either multiple bulbs surrounding thepart with individual control or the ability for the thermal source tomove would be the required setup. For analysis, both could beaccomplished. The reflective thermography would be analyzed during thepulse simultaneously and then subsurface defects after the pulse wouldbe analyzed after allowing time for the peak contrast slopes to occur.Ultimately, the dual method will greatly minimize the chance of defectsarising in final products after completion of the build.

In various embodiments, present invention provides a system and methodfor using both reflected and absorbed light to get dual informationabout the surface and subsurface of an ABS 3D printed part by orientingthe light sources and the camera so that the incident light on thedefault surface does not reflect specularly into the IR sensor. The IRreflected light is used to detect fine surface roughness (smaller thanthe lines of the deposition paths) and to detect small gaps in thesurface that may reduce the strength or cause other problems in theprinted part, such as shorts in electrical lines printed over them. Thereflected light in the infrared is then used to characterize surfacefeatures.

When parts are fabricated using Binder Jetting (BJ), there are manyvariables that can alter both the mechanical and thermal properties ofthe product. The powder size distribution can affect overall density ofthe part, which can have a major impact on green part strength,sintering shrinkage and thermal properties. The layer thickness and partorientation can vary. Because the binder is dropped from an inkjet headonto the part, the printing speed has mechanical effects on the finalpart. Additionally, binder saturation levels can affect mechanicalstrength, thermal diffusivity and even dimensional accuracy, dependingupon the saturation levels.

Research regarding the variable BJ parameters has primary been focusedon the improvement of mechanical properties and density. There has beenlittle research on the thermal effects these parameters have on BJparts, specifically binder saturation and curing temperatures. Theability to monitor and understand thermal properties, specificallythermal diffusivity, during the build could help in improving processparameters, such as the pre-binder heating process, which is done tohelp cure some of the binder before the next layer of powder is laid.Another important aspect is that once an approximate thermal diffusivityis known based on specific parameters, then future parts with the sameparameters can be monitored to locate defects and quantify the defectdepth. Thus, an understanding of the capabilities of using the longerpulse method of PT to compare how the curing temperature and densityaffect the thermal diffusivity of BJ parts and raw powder is valuable toimproving the quality of BJ parts.

Using the method of PT, defects can be quantified based on the timeresponse of the surface temperature to a specific depth. The capabilityof this however, is limited to the knowledge of the thermal diffusivityof the material being tested. Because the binder saturation percentageand density can vary with different parameters in BJ, the thermaldiffusivity of the part being built is an unknown variable. However, ifthere is a known “defect” depth, the method of PT can then be used todetermine the thermal diffusivity. This can be done by rearranging thedefect depth quantification equations, to give thermal diffusivity:

$\begin{matrix}{\alpha = \frac{3.64L^{2}}{t_{s}\pi^{2}}} & (10) \\{\alpha = \frac{L^{2}}{t_{2}\pi}} & (11)\end{matrix}$

with L being the known “defect” depth, t_(s) is the peak slope contrasttime and t₂ the log second derivative peak slope time. Either method canbe used to determine the defect depth, however, it is important to notethat each equation is specific to the method it is defined for. For thepurposes of this exemplary embodiment, the peak temperature contrastmethod will be used for all the thermal diffusivity measurements. Thereason for this is that a more refined model is needed to utilize thelog second derivative method, as was previously discussed.

To measure and compare the difference between the apparent and tappeddensity of 30 μm 420 SS powder, a fixture was made with a hollowedcylinder in the center to capture the powder, as shown in FIG. 22. Forpowders and granules, the apparent (bulk) density can be determined bythe ratio of the mass to a given volume. The tapped density is obtainedby mechanically tapping a graduated cylinder containing the sample untillittle further volume change is observed.

In this exemplary embodiment, the cylinder in the center has an innerdiameter of 19.5 mm and a height of 9 mm. For the apparent density, thepowder was poured into the container until it filled the top of thecylinder and then a blade was used to level off the surface. A metaltube was then inserted into the cylinder to separate the powder used fordensity measurement from the extra powder that spilled over duringfilling. The powder inside the center region was weighted with anAdventurer SL AS214 scale with a resolution of 0.0001 g. To calculatethe powder density, the powder volume was calculated from the cylinderdiameter and height.

For the tapped density, the same process was used for the weight of thepowder. However, as the powder was poured into the fixture, the fixturewas vibrated by hand, back and forth steadily allowing for the powder tosettle. This is different than the proposed method of obtaining a tapdensity by tapping a cylinder 1000 to 3000 cycles at approximately 284cycles per minute. However, for the purposes of preliminaryinvestigation on the affects density has on thermal diffusivity, thisvibrational method will suffice.

To determine the thermal diffusivity of a material using PT, there mustbe a known defect depth to slow down the heat conduction process andproduce a thermal contrast on the surface. With raw powder however, thisis especially difficult because normally, the defects that have beenused are voids. For the case of raw powder, this type of defect is notpossible, as the powder would simply fill the void space, as there is nomechanical structure to the powder. Therefore, to measure the thermaldiffusivity, a fixture was made to support the powder and simulate thedefect. The fixture was FDM printed Polylactic acid (PLA), the othermost common material printed with the FDM process, next to ABS. Thebenefit of using PLA as the fixture (and known defect depth) is that thematerial has a very low thermal diffusivity of 5.556×10⁻⁸ (m² s⁻¹). Thebenefit of having a very small thermal diffusivity material as thefixture and known defect depth is that it increases the possibility of athermal contrast after the pulse. If the thermal diffusivity of twomaterials are similar, then the heat transfer rate doesn't slow down,and no thermal contrast is produced on the surface. Thus, the larger thedifference in thermal diffusivities between the material and the defect,the larger the thermal contrast.

To obtain thermal diffusivity measurements, the same process is used tocreate the apparent and tapped density. However, this time the powderwas leveled off at the top of the fixture as seen in FIG. 23, therebycreating the defect in the center. To maximize the thermal contrast evenfurther and to ensure that no powder falls between the roads of the FDMprinted part, a piece of clear tape was placed over the defect. Thethickness of the tape was 0.045 mm therefore, for the thermaldiffusivity calculations, 0.955 mm was the actual defect used in thecalculations.

To understand the effect that curing temperature has on the thermaldiffusivity of a BJ green part, two parts made of 420SS with dimensionsas seen in FIG. 24 were built on an ExOne Innovent 3D printer. The sameparameters were used for each part to ensure that the only variable wasthe post process curing temperature. The parameters can be seen in TableIII.

TABLE III Build Parameters for Thermal Diffusivity Testing of Binder JetParts Drying Time (s)   12 Emitter Output (%)  100 Target Bed Temp.(°C.)   40 Recoat Speed (mm/s)   90 Oscillator Speed (rpm) 2200 RollerTraverse Speed (mm/s)    5 Roller Rotation Speed (rpm)  300 DesiredSaturation (%)   60 Layer Thickness (μm)  100 Curing Temperature (°C.)165 185 Curing time (hours)   4

The known 8×8 mm defect depths of each part ranged from a depth of0.5-1.0 mm and each defect was used to calculate thermal diffusivity.Multiple defect depths were used to understand if there was any effecton the calculated diffusivity with increased layers as each depth isseparated by one-layer thickness. To ensure accurate calculated thermaldiffusivities, the deeper defects (0.9-1.0 mm) were used to calculatethe thermal diffusivity first, then the maximum allowable pulse lengthswere calculated for the rest of the defect depths. With the knownmaximum allowable pulse lengths for each depth, thermal diffusivitieswere calculated.

The density measurement results can be seen in FIG. 25. The apparentfractional packing density averaged approximately 55% of the overalldensity for 420 SS of 7740 (kg m⁻³). In comparison the tapped fractionalpacking density averaged 59% of the overall density. This is in closerelation to the tap density for monosized spherical powder of 60-64%obtained using an alternative method known in the art. If the othermethod had been used, an increase in tap density may have been seen,however, this 4% difference is sufficient for thermal diffusivitycomparison purposes.

It can be seen from FIG. 25 that the tapped density of raw 420 SS powderhas a larger thermal diffusivity. The density increase was approximately4%, however, from apparent to tap density, the average thermaldiffusivity increases approximately 23%. With a specific heat remainingconstant, this means that the thermal conductivity increased over 30%between the two densities. The significant increase in thermalconductivity and diffusivity is because with the tap density, theinterstitial void spaces between the powder particles is minimized, thusincreasing the contact area between particles. The increase in contactarea allows for a faster conductive heat transfer rate through thepowder down to the defect depth.

Pulse flashes ranging from 0.6 to 1.0 s were used to thermally exciteeach part. Once an average thermal diffusivity was calculated for thedeeper defects, the maximum allowable pulse length was used for each ofthe shallower defects to calculate thermal diffusivity. The results canbe seen in FIG. 26. For the 165° C. cured part, the thermal diffusivitywas small enough that all the defect depths, except for 0.5 mm, wereable to be accurately calculated within the range of pulse lengths.However, the significant increase in thermal diffusivity from curing thepart at 185° C. only allowed for the 0.8-1.0 mm defects to be used forthermal diffusivity calculation. To measure the shallower defects due tothe increase in thermal diffusivity, shorter pulse lengths must be usedas not to exceed the maximum allowable pulse length. The reason for theincrease in thermal diffusivity when curing at 185° C. is believed to bebecause the increase in temperature is needed for the binder to fullyset among the powder particles in the part. At 185° C., the fully setbinder becomes crosslinked between the powder particles thus minimizingthe surface contact heat resistance that occurs between powders simplytouching. In turn, increasing the heat conduction mechanism within thepart. FIG. 27 is an illustration of a thermal diffusivity comparisonbetween two binder jet parts, one cured at 165° C. and the other at 185°C., in accordance with an embodiment of the present invention.

The overall average thermal diffusivity of the 185° C. cured part isapproximately 1.5 times larger (150%) than the thermal diffusivity ofthe part cured at 165° C. In comparison, the density only increased thethermal diffusivity of raw powder by 22%. FIG. 28 shows how critical thecuring temperature is to thermal diffusivity as the increase due totemperature difference is more than the increase from raw powder to the165° C. cured part.

Based on the results, using the longer pulse method of PT, thermaldiffusivity values of BJ parts can be calculated based on a known defect(feature) depth. It is important to ensure that when using the longerpulse method, that the pulse length does not exceed the maximumallowable pulse length. Thus, for quantification of thermal diffusivity,deeper defects are ideal as the pulse times have a much larger range.The 4% increase between apparent and tapped density had a 22% increasein thermal diffusivity due to the increased contact points between thepowder particles. Yet the curing temperature had the biggest impact onthermal diffusivity. Causing an increase in thermal diffusivity ofapproximately 150% between 165° C. curing temperature and 185° C.Understanding how the BJ process parameters effect the thermaldiffusivity of the green part could have significant quality benefits.In addition to detecting defects, systematic variation in packingdensity and binder saturation can be monitored and quantified during thebuild. Parameters such as drying time or bed temperature can beoptimized, or even close loop controlled, to increase build times andminimize energy use.

The present invention allows for the use of lower cost, slow response,bulbs that have less demanding power supply requirements, therebyreducing the overall cost of the monitoring system. Such animplementation is feasible due to the relatively low thermalconductivity of the components being printed. Ongoing work includes,providing patterned heating of the top surface, utilize the evolution ofthis pattern on the top surface to identify variations in the propertiesof the material in the plane, extracting thermal diffusivity data frompoints on the plane using this approach and the estimation of otherproperties of interest in specific applications based on theircorrelation with thermal diffusivity. Potential parameters of interestinclude, relative density of a porous material or powder, electricalconductivity, modulus of elasticity and strength.

Hardware and Software Examples

The various techniques described herein can be implemented in connectionwith hardware or software or, where appropriate, with a combination ofboth. Thus, the methods and system described herein, or certain aspectsor portions thereof, can take the form of program code (i.e.,instructions) embodied in tangible media, such as hard drives, solidstate drives, or any other machine-readable storage medium, wherein,when the program code is loaded into and executed by a machine, such asa computing device, the machine becomes an apparatus for practicing theinvention. In the case of program code execution on programmablecomputers, the computing device will generally include a processor, astorage medium readable by the processor (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device. The program(s) can be implemented inassembly or machine language, if desired. In any case, the language canbe a compiled or interpreted language, and combined with hardwareimplementations.

The invention can also be practiced via communications embodied in theform of program code that is transmitted over some transmission medium,such as over electrical wiring or cabling, through fiber optics, or viaany other form of transmission, wherein, when the program code isreceived and loaded into and executed by a machine, such as an EPROM, agate array, a programmable logic device (PLD), a client computer, or thelike, the machine becomes an apparatus for practicing the invention.When implemented on a general-purpose processor, the program codecombines with the processor to provide a unique apparatus that operatesto invoke the functionality of the invention. Additionally, any storagetechniques used in connection with the invention can be a combination ofhardware and software.

It will be seen that the advantages set forth above, and those madeapparent from the foregoing description, are efficiently attained andsince certain changes may be made in the above construction withoutdeparting from the scope of the invention, it is intended that allmatters contained in the foregoing description or shown in theaccompanying drawings shall be interpreted as illustrative and not in alimiting sense.

It is also to be understood that the following claims are intended tocover all of the generic and specific features of the invention hereindescribed, and all statements of the scope of the invention which, as amatter of language, might be said to fall therebetween. Now that theinvention has been described,

What is claimed is:
 1. A method for monitoring a layer-basedmanufacturing process, the method comprising: exposing a surface layerof a 3D part undergoing layer-based manufacturing to a thermal energypulse from a thermal energy source, the thermal energy pulse having apulse duration that is based upon a thermal diffusivity of a material ofthe 3D part and a depth of a subsurface defect being detected in the 3Dpart; measuring a surface temperature of the surface layer of the 3Dpart with an infrared (IR) camera, in response to absorption of thethermal energy pulse into the 3D part, to identify a location of thesubsurface defect in the 3D part; and calculating the depth of thesubsurface defect in the 3D part.
 2. The method of claim 1, whereincalculating a depth of the subsurface defect in the 3D part furthercomprises, performing a peak temperature contrast slope method tocalculate the depth.
 3. The method of claim 1, wherein calculating adepth of the subsurface defect in the 3D part further comprises,performing a log second derivative method to calculate the depth.
 4. Themethod of claim 1, wherein measuring a surface temperature of thesurface layer of the part with an infrared (IR) camera to identify alocation of a subsurface defect in the 3D part, further comprises:measuring a surface temperature above a defect-free area of the 3D part;measuring a surface temperature above a defect area of the 3D part;determining a peak temperature contrast slope time based upon thesurface temperature above a defect-free area and the surface temperatureabove a subsurface defect; and wherein calculating a depth of thesubsurface defect in the 3D part, further comprises calculating thedepth of the subsurface defect based upon the peak temperature contrastslope time, wherein the peak temperature contrast slope time is directlyproportional to the square of the depth of the subsurface defect.
 5. Themethod of claim 4, wherein the peak temperature contrast slope time isdetermined beginning at one half of the duration of the thermal pulse.6. The method of claim 1, wherein a maximum pulse length of the thermalenergy pulse is determined by:$\tau_{s} = {\frac{t_{p}}{t_{s}} = \frac{\pi^{2}\alpha\; t_{p}}{3.64\; L^{2}}}$wherein, t_(p) is the pulse length, t_(s) is a peak temperature contrastslope time, L is the defect depth of interest and a is the thermaldiffusivity of the part.
 7. The method of claim 1, wherein a maximumallowable pulse duration of the thermal energy pulse is determined by:$\tau_{2} = {\frac{t_{p}}{t_{2}} = \frac{{\pi\alpha}\; t_{p}}{L^{2}}}$wherein, t_(p) is the pulse length, t₂ is a peak second derivative time,L is the defect depth of interest and α is the thermal diffusivity ofthe part.
 8. The method of claim 1, wherein the pulse duration comprisesa range of pulse durations.
 9. The method of claim 1, furthercomprising, for each layer of the layer-based manufacturing process:orienting the thermal energy source and the IR camera to minimizespecular reflections from a defect-free surface area of the 3D part fromreaching the IR camera, in response to the thermal energy pulse;blocking the thermal energy pulse from the part following exposing thesurface layer of a 3D part undergoing layer-based manufacturing to thethermal energy pulse; and detecting specular reflections from a surfacedefect on a layer of the 3D part at the IR camera to identify a locationof the surface defect on the layer of the 3D part.
 10. The method ofclaim 9, wherein the layer-based manufacturing process is a FusedDeposition Modeling (FDM) process resulting in a plurality of depositionroads and wherein the thermal energy source is positioned in parallelwith the deposition roads.
 11. The method of claim 1, further comprisingcalculating the thermal diffusivity of the 3D part based upon one ormore detected subsurface features.
 12. The method of claim 11, whereinthe layer-based manufacturing process comprises one or more controlparameters and wherein the thermal diffusivity of the part is used toadjust the one or more control parameters.
 13. The method of claim 1,wherein the layer-based manufacturing process is selected from FusedDeposition Method (FDM), Powder Bed Fusion (PBF) and Binder Jetting(BJ).
 14. A method for monitoring a layer-based manufacturing process,the method comprising: exposing a surface layer of a 3D part undergoinglayer-based manufacturing to a thermal energy pulse from a thermalenergy source, the thermal energy pulse having a pulse duration that isbased upon a thermal diffusivity of a material of the 3D part and adepth of a subsurface defect being detected in the 3D part, wherein thethermal energy source and the IR camera are oriented to minimizespecular reflections from a defect-free surface area of the 3D part fromreaching the IR camera, in response to the thermal energy pulse;measuring a surface temperature of the surface layer of the part with aninfrared (IR) camera, in response to absorption of the thermal energypulse into the 3D part, to identify a location of a subsurface defect inthe 3D part; calculating the depth of the subsurface defect in the 3Dpart; blocking radiant heat resulting from the thermal energy pulse fromreaching the 3D part following exposing the surface layer of the 3D partundergoing layer-based manufacturing to the thermal energy pulse; andfor each layer of the 3D part, detecting specular reflections from adefect on the surface layer of the 3D part at the IR camera to identifya location of the surface defect on the layer of the 3D part.
 15. Amethod for monitoring each layer in layer-based manufacturing process,the method comprising: orienting a thermal energy source and an IRcamera relative to a 3D part undergoing a layer-based manufacturingprocess to minimize specular reflections from a defect-free surface areaof the 3D part from reaching the IR camera; exposing a surface layer ofthe 3D part undergoing layer-based manufacturing to a thermal energypulse from the thermal energy source, the thermal energy pulse having apulse duration that is based upon a thermal diffusivity of a material ofthe 3D part and a depth of a subsurface defect being detected in the 3Dpart; blocking radiant heat resulting from the thermal energy pulse fromreaching the 3D part following exposing the surface layer of the 3D partundergoing layer-based manufacturing to the thermal energy pulse; anddetecting specular reflections from a layer of the 3D part at the IRcamera to identify a location of a surface defect on the layer of the 3Dpart.
 16. A system for monitoring a layer-based manufacturing process,the system comprising: one or more thermal energy sources for generatinga thermal energy pulse, the thermal energy pulse incident on a surfacelayer of a 3D part undergoing layer-based manufacturing, the thermalenergy pulse having a pulse duration that is based upon a thermaldiffusivity of a material of the 3D part and a depth of a subsurfacedefect being detected in the 3D part; at least one infrared (IR) camerapositioned to measure a surface temperature of the surface layer of the3D part, in response to absorption of the thermal energy pulse into the3D part, to identify a location of a subsurface defect in the 3D part;and a processor for calculating the depth of the subsurface defect inthe 3D part.
 17. The system of claim 16, further comprising, one or moreradiant heat shields positioned between the thermal energy source andthe surface of the 3D part to block radiant heat from the thermal energysource from reaching the 3D part, following the pulse duration of thethermal energy pulse.
 18. The system of claim 16, wherein the one ormore thermal energy sources and the at least one IR camera are orientedto minimize specular reflections from a defect-free surface area of the3D part from reaching the IR camera in response to the thermal energypulse; and wherein the at least one IR camera is positioned to detectspecular reflections from defects on the current layer of the 3D part toidentify a location of a surface defect on the layer of the 3D part. 19.The system of claim 18, wherein the at least one IR camera used todetect specular reflections is the same IR camera used to measure asurface temperature.
 20. The system of claim 16, wherein the processoris further for calculating the thermal diffusivity of the part basedupon one or more detected subsurface features.
 21. The system of claim20, wherein the layer-based manufacturing process comprises one or morecontrol parameters and wherein the measured thermal diffusivity of thepart is used to adjust the one or more control parameters.
 22. Thesystem of claim 16, wherein the layer-based manufacturing process isselected from Fused Deposition Method (FDM), Powder Bed Fusion (PBF) andBinder Jetting (BJ).
 23. The system of claim 16, wherein the maximumallowable pulse duration comprises a range of pulse durations.
 24. Thesystem of claim 16, wherein the processor determines a maximum allowablepulse duration (t_(d)) of the thermal energy pulse by adding one half ofthe pulse duration t_(p) to the calculated peak temperature t_(s) forcalculating the defect depth using a peak contrast slope method inwhich: $t_{s} = \frac{3.64\; L^{2}}{\pi^{2}\alpha}$ wherein, L is adefect depth of interest and a is a thermal diffusivity of the part. 25.The system of claim 16, wherein the processor determines a maximumallowable pulse duration (t_(d)) of the thermal energy pulse by addingone half of the pulse duration t_(p) to the calculated peak temperaturet₂ for calculating the defect depth using a log second derivative methodin which: $t_{2} = \frac{L^{2}}{\pi\;\alpha}$ wherein, L is the defectdepth of interest and a is the thermal diffusivity of the part.
 26. Thesystem of claim 16, wherein the one or more thermal energy sources arehalogen bulbs.
 27. A system for monitoring each layer in a layer-basedmanufacturing process, the system comprising: one or more thermal energysources positioned to expose a surface layer of a 3D part undergoinglayer-based manufacturing to a thermal energy pulse, the thermal energypulse having a pulse duration that is based upon a thermal diffusivityof a material of the 3D part and a depth of a subsurface defect beingdetected in the 3D part; one or more radiant heat shields positionedbetween the thermal energy source and the surface of the 3D part toblock radiant heat from the thermal energy source from reaching the 3Dpart, following the pulse duration of the thermal energy pulse; and atleast one infrared (IR) camera, wherein the one or more thermal energysources and the at least one IR camera are oriented to minimize specularreflections from a defect-free surface area of the 3D part from reachingthe IR camera in response to the thermal energy pulse and wherein the atleast one IR camera is positioned to detect specular reflections fromdefects on the surface layer of the 3D part to identify a location of asurface defect on the layer of the 3D part.